Adaptive learning system with a product configuration engine

ABSTRACT

The adaptive learning systems described herein may include machine-learning engines, product configuration engines, and/or various other components configured to improve the efficiency of processing transactions. The systems described herein may detect and/or predict declined transactions, token deficiencies, insufficient system capacities, and/or other system anomalies. As such, the system may perform operations to generate additional tokens associated with assets, provision various bin ranges, and/or share reserve capacities, and/or other operations. Thus, the system may improve system efficiencies, ensure reliability and operability across the system, and optimize the operations for successfully processing transactions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.15/248,228, filed Aug. 26, 2016, which is herein incorporated byreference in its entirety.

BACKGROUND

The present invention generally relates to machine learning,particularly with product configuration engines for electronic dataprocessing.

Processing a transaction generally involves the movement of assets. Insome respects, the movement of the assets may vary based on the entitiesinvolved, the one or more types of transactions required by themovement, and/or the various instruments available, among otherpotential attributes. Further, the movement may be characterized basedon the sources and/or sinks of the movement, various regionalregulations, multiple currencies that may be involved in the movement,and/or the types of assets such as the products associated with themovement, among other attributes as well. In some instances, numerousattributes may be determined to qualify the movement internally andexternally from the perspective of a participating entity involved inthe transaction.

In various circumstances, the number of records maintained by hardwareprocessors, memories, and/or data storage components may proportionallyincrease to thousands, millions, and/or possibly billions of fields,potentially based on the increasing number of attributes describedabove. Thus, in some instances, numerous fields may be associated with asimple transaction such that, for example, each of the participatingentities involved may provide their respective approvals. Further, thenumerous fields clustered with the transaction may create a number ofsystem inefficiencies. Thus, as demonstrated in the scenarios above,there may be various inefficiencies associated with systems that handlelarger volumes of fields associated with transactions. Further, it maybe required to reduce and/or eliminate the latency involved withprocessing the transactions based on user experience requirements,service level agreements, and/or market demands and costs, among otherpossible factors.

As such, there is much need for technological advancements in variousaspects of computer technology in the realm of computer networks andparticularly with systems associated with transactions to optimize themanagement of data amongst the participating entities to improve systemperformance and efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an exemplary system, according to an embodiment;

FIG. 1B illustrates the exemplary system with product-policy data,according to an embodiment;

FIG. 1C illustrates an product configuration engine (PCE) with matrices,according to an embodiment;

FIG. 2A illustrates an exemplary system, according to an embodiment;

FIG. 2B illustrates the exemplary system with product-policy data,according to an embodiment;

FIG. 2C illustrates an product configuration engine (PCE) with matrices,according to an embodiment;

FIG. 3 illustrates an exemplary system with transfer data, according toan embodiment;

FIG. 4 illustrates an exemplary system, according to an embodiment;

FIG. 5 illustrates an exemplary method, according to an embodiment;

FIG. 6 is a simplified block diagram of an exemplary system, accordingto an embodiment; and

FIG. 7 illustrates a user device, according to an embodiment.

Embodiments of the present disclosure and their advantages may beunderstood by referring to the detailed description herein. It should beappreciated that reference numerals may be used to illustrate variouselements and features provided in the figures. The figures mayillustrate various examples for purposes of illustration and explanationrelated to the embodiments of the present disclosure and not forpurposes of any limitation.

DETAILED DESCRIPTION

As described above, there may be various inefficiencies associated withsystems that handle a larger number of fields associated withtransactions. Yet, there may also be an increasing number ofcomplexities associated with the transactions, including intricateassets such as various types of products, changes in time domains,and/or the number of currencies involved, among the number of possibleattributes, as similarly described above. Thus, the requirement toreduce and/or eliminate the latency involved with processing thetransactions may be even more challenging.

In view of the circumstances above, an adaptive learning system with aproduct configuration engine is described herein. The system may beconfigured to consolidate attributes of the transactions, unify thestructures of the transactions, and/or reduce the complexities describedabove, among various other operations described herein to improve thesystem performance and efficiencies. Thus, the system may be scalablefor further requirements, thereby creating a product modeling systemsustainable for numerous attributes that may be added and/or removed.

For example, in some instances, the system may be configured to managetoken issuances. Examples of such tokens may include network tokens,transfer tokens, payment tokens, identity tokens, and/or various typesof tokens that identify assets, possibly uniquely identifying suchassets, where the assets including resources, limited resources, and/orscarce resources, among other possibilities. In some instances, thetokens may be issued from particular bins, possibly based on thefrequency of tokens issued. Further, the bin ranges associated with apool of tokens may deplete at faster rates than expected and/orprojected by the system. As such, the system may determine the number oftokens in the bin ranges. Further, based on analyzing the tokendepletion rate, the system may provision one or more additional binranges to generate more tokens, thereby eliminating potential tokendeficiencies that may cause transaction to be declined. Thus, the systemoperates proactively to eliminate the deficiencies and related latenciesinvolved with waiting for provisioning bin ranges and issuing tokensbased on token deficiencies. Yet further, the system eliminates thepossibility of human errors involved with provisioning the bin ranges,generating the tokens, and/or other actions associated with tokenmanagement. Notably, in some instances, the bin, as described herein,may refer to a bank identification number and/or a “BIN.” For example,the bin may be related to the International Organization forStandardization (ISO), such as the ISO/IEC 7812, part 1, thatcorresponds to the numbering systems. Yet further, the bin, as describedherein, may also refer to an issuer identification number and/or an“IIN,” possibly represented by six digits that may also indicate themajor industry identifier, where the IIN may identify the issuingorganization.

Notably, consider one scenario in which a user's transaction is declinedbased on a deficiency in the number of tokens available fortransactions. In such a scenario, the system described herein isconfigured to proactively prevent the transaction from being declinedbased on the system detecting the number of tokens available at anygiven time and also the token depletion rate associated with theon-going transactions. As noted, the system is able to predict thepossibility of a token deficiency based on the number of tokens assignedor issued over the number of tokens generated in a given time period. Assuch, the user transaction may be processed without any decliningerrors. In various such ways, the end users may be directly affected bythe system's ability and optimization of managing tokens.

In some embodiments, the system may process feedback data from theadaptive learning engine to perform one or more operations. In someinstances, the system generates an intermediate matrix that representsvarious policy requirements, such as the attributes of the transactionsdescribed above. The intermediate matrix may represent the policyrequirements in binary form, possibly based on the feedback data fromthe adaptive learning engine. In some instances, the intermediate matrixmay be used to perform matrix operations to modify product-policy datastored in the system. In some instances, the product-policy data mayrepresent one or more assets, such as one or more products, and variouspolicies associated with the assets and/or products, possibly where thepolicies include various restrictions and/or constraints on transactionsassociated with the assets and/or products. Thus, the system may be ableto learn various policy requirements based on the feedback data from theadaptive learning engine and implement modifications to reduce thenumber of records maintained, reduce delays and/or latencies involvedwith system maintenance, and further reduce human interactions with thesystem, among other possible sources of inefficiencies.

In some embodiments, the system may enhance authentications with onlinetransactions. For example, consider a scenario in which the systemdetects a number of transactions being declined, possibly based onvarious instruments associated with a particular issuing system referredto herein as an issuer. In particular, the system may detect one or moreindications of anomalies based on the number of transactions declined,potentially detecting a temporary increase in the number of transactionsdeclined, the types of transactions declined, insufficient systemcapacities to process the transactions, and/or the conditions on thesystem related to the transactions declined, among other possibleindications of the anomalies. In such instances, the system may beconfigured to notify system administrators, possibly displayingnotifications on one or more mobile devices of the administrators.Further, the system may modify various policy requirements, potentiallywith the intermediate matrix described above. As such, the system mayenable the transactions to be processed based on the variousmodifications implemented, thereby reducing delays and/or latencies, thenumber of records maintained, and further various steps for the systemto reattempt and/or reprocess the declined transactions.

In some embodiments, the system may also be configured to sharetransactional capacities. For example, in some instances, the capacityto process transactions may vary based on a given period of time.Further, the system may determine the operating capacity of the systemrequired to process the transactions over the given period of time. Yetfurther, the system may determine a reserve capacity that may beavailable based on the operating capacity during the given period oftime. For example, in some instances, the reserve capacity may bedetermined based on the feedback data from the adaptive learning engine.Further, the system may identify a number of system sites that requireadditional capacity. As such, the system may share the reserve capacitywith the sites that require the additional capacity. Thus, the systemmay efficiently allocate its capacities to ensure operability acrossvarious other sites and/or networks.

FIG. 1A illustrates an exemplary system 100, according to an embodiment.As shown, the system 100 may include an adaptive learning engine (ALE)102, a product configuration engine (PCE) 104, and a platform 106.Further, there may be a connection 108 between the ALE 102 and theplatform 106, a connection 110 between the ALE 102 and the PCE 104, anda connection 112 between the PCE 104 and the platform 106.

In some embodiments, the ALE 102 may be a self-learning engine. Inparticular, the ALE 102 may learn and/or develop a number of rules basedon data retrieved from the connections 108 and/or 110 from platform 106and/or the PCE 104, respectively. Further, the ALE 102 may performmachine learning based on various self-learning mechanisms associatedwith detecting network anomalies, such as those described above, andapplying a number of intelligent rules. Further, the platform 106 may beused and/or implemented by a provider, such as PayPal, Inc. of San Jose,Calif., USA. The platform 106 may include an activity monitor configuredto detect various indications of transactional traffic associated withone or more networks, such as transaction requests transmitted and/orreceived, and further token traffic associated with the requests. Yetfurther, the PCE 104 may include various product-policy data entries. Insome instances, the product-policy data may be configured automaticallybased on various actions, such as rule actions and/or manual actions,among other possible actions contemplated herein.

In some embodiments, the ALE 102 may retrieve data from the platform 106over the connection 108 and develop various asset, product, and/orpolicy rules. Further, the rules may be transmitted to the PCE 104 overthe connection 110. In some instances, the platform 106 may implementvarious operations based on the product-policy data in the PCE 104. Assuch, the system 100 may operate in a cyclical manner, where the ALE 102optimizes the system 100 based on the various self-learning mechanismsassociated with detecting the network anomalies, insufficient systemcapacities to process transactions, and developing the product-policydata.

FIG. 1B illustrates the exemplary system 100 with the product-policydata 130, 132, and 134, according to an embodiment. As shown, the system100 includes the ALE 102, the PCE 104, and the connection 110 betweenthe ALE 102 and the PCE 104, as described above in relation to FIG. 1A.Further, the system 100 includes a token database 118 and a connection122 between the token database 118 and the PCE 104. Yet further, aconnection 120 is shown between the token database 118 and the ALE 102,where the token database 118 may be implemented with the platform 106described above.

In some embodiments, the system 100 may identify one or more tokenrequests, possibly from the platform 106 described above in relation toFIG. 1A. For example, the token requests identified may correspond withtransactions requested by users of the platform 106, possibly atransaction to move the assets described above. In some instances, thesystem 100 may also determine an indication of token traffic associatedwith the platform 106, possibly based on the identified token requestand/or a group of identified token requests. Further, the system 100 maydetermine a token depletion rate based on the number of tokens issued inresponse to the token requests over a given period of time. As such, thesystem 100 may generate a number of tokens in the token database 118based on the token depletion rate, possibly to avoid the scenariosdescribed above associated with token deficiencies potentially causingtransactions to be declined. Yet further, the system 100 may learn oneor more indications of anomalies associated with the transactionsrequested by users and further cause mobile devices, such as thosecontrolled by system administrators, to display the indications. Forexample, the indications of the anomalies may include a time of day, atime of the year (e.g., holidays), a number of transactions at a giventime period, the entities involved with the transactions, insufficientsystem capacities to process transactions, and/or various otherconditions of the system 100, among others as contemplated above. Assuch, the system 100 may learn to prevent possibilities of tokendeficiencies as described above.

Notably, under various circumstances, the system 100 may measure thetoken depletion rate from the activity monitor provided by the platform106. Yet, in some instances, the possibility of token deficiencies maybe cured as the system 100 generates additional tokens or tokenidentifiers. Further, in some instances, the system 100 may measure thedepletion rate of one or more token ranges from the perspective of anissuer and/or an issuer system. As such, the activity monitor of theplatform 106 may detect the generation of the tokens to meet, dropbelow, and/or exceed a token threshold. As such, the adaptive learningengine 102 may activate additional bin ranges for the tokens such thatthe issuer system may issue token numbers and/or identifiers from thesecond bin range to avoid the potential deficiencies, possibly resultingin declined transactions.

In some embodiments, the system 100 may also optimize the processing oftransactional payloads by identifying the specific elements required forprocessing the transaction, where the elements may include variousattributes of the transaction described above. For example, the system100 may identify core elements that may be required to move the assets,including various types of products, possibly associated with thetransaction. In particular, the core elements may identify the buyer,the seller, and/or the consumer, among other possible entities involvedin the transaction. Yet further, the core elements may identify variousdetails of the interactions including the purchase of goods, theservices provided, the movement of assets, e.g., money or funds, and/oradding one or more financial instruments identified in a digital wallet.In addition, the core elements may identify the source of thetransaction, the type of assets involved in the transaction, and/or thevarious instruments, e.g., credit cards, debit cards, and/or loaninstruments. Yet further, the core elements may indicate the approvingentities, such as approving agents, merchants, banks, issuers and/orloan approvers, among other possible entities involved.

In some embodiments, the system 100 may also identify profile elements.For example, the profile elements may indicate a number of decisionsand/or actions, such as enabling actions, disabling actions, and/orfiltering actions, among other actions contemplated above. Further, theactions may be related to regulatory compliance, card network policies,fraud and/or risk, service provider policies, required entityacceptances, among other possible factors. For example, the enablers mayinclude channel partners, such as in-store merchants. As such, theenabling actions may indicate various transactional processes such ascheckout processes, near-field communication (NFC) transactionalprocesses, biometric data (e.g., fingerprint data) authenticatedtransactional processes, and/or other processes potentially associatedwith instruments accepted by the merchants. Further, the disablers mayindicate compliance rules, such as rules by country, funding type,and/or various markets, among other potential restrictions and/orconstraints. Yet further, the filters and/or filtering actions mayinclude various risk rules for the transaction, the funding types, theparticipating entities, and/or the various markets.

In some embodiments, the system 100 may break down the common productelements to derive a set of the core elements described above. Bystandardizing and/or normalizing the elements, the system 100 mayidentify unnecessary elements, remove such elements, and/or eliminateredundancies to improve system efficiency. As such, the system 100 maylist the profile elements based on the break down methodology and thestandardization and/or normalization processes. Yet further, the system100 may synthesize the various actions, including the enabling actions,the disabling actions, and/or the filtering actions as well. As such,the system 100 may create and/or manage the product configuration engine104 to make changes to various elements, such as the core elements andthe profile elements described above.

FIG. 1C illustrates the product configuration engine (PCE) 104 withmatrices 150A, 150B, and 150C, according to an embodiment. As noted, thesystem 100 optimizes various forms of transfer data, such as transactionpayloads, by identifying the elements required to process thetransactions. As such, the system is capable of avoiding redundanciesand/or duplications, thereby saving excess CPU cycles of the system 100to optimize the system performance and improve system efficiency. Thus,the system 100 may provision a multi-dimensional matrix 150A configuredto map core and/or profile elements required in a transaction,potentially based on the products associated with the transaction.

For example, the matrix 150A may be a representation of a number ofvalues associated with the core and/or profile elements. As shown, thematrix 150A is represented with product-policy data 130A in the firstrow including the data (a11), data (a12), data (a13), and other datacontemplated with the ellipses, and further the data (a1n), where the“n” represents the number of columns and the number of rows.Product-policy data 132A corresponds to the second row including thedata (ai1), data (ai2), data (ai3), and other data contemplated with theellipses, and further the data (ain), where “i” indicates the particularrow in the matrix 150A. Product-policy data 134A corresponds to thethird row including the data (an1), data (an2), data (an3), and otherdata contemplated with the ellipses, and further the data (ann).

Further, the matrix 150B may also be a multi-dimensional matrix,possibly formed with data from the adaptive learning engine 102 over theconnection 110, as described above. The matrix 150B may be arepresentation of decision actions in one or more policy domainsprovided in binary form. For example, the value of “1” may indicate thatan element is required and the value of “0” may indicate that an elementis not required. Thus, the matrix 150B may be made up of 1's and 0's topass elements required and mask elements that are not required. Asshown, the matrix 150B is represented with a first row including thedata (b11), data (b12), data (b1j), and other data contemplated with theellipses, and further the data (bin), where “j” is the any numberbetween 3 and n, and where “n” represents the number of columns and thenumber of rows. The second row may include the data (bi1), data (bi2),data (bij), and other data contemplated with the ellipses, and furtherthe data (bin), where “i” indicates the particular row in the matrix150B. The third row may include the data (bn1), data (bn2), data (bnj),and other data contemplated with the ellipses, and further the data(bnn). In some instances, the PCE 104 and/or the ALE 102 may addadditional rows and/or columns based on the matrix 150A, possibly tomodify the number of rows and/or columns that correspond to the numberof rows and/or columns in the matrix 150A. For example, the PCE 104 mayadd a row to the matrix 150A based on a new policy that that isgenerated and further, the PCE 104 may remove a row from the matrix 150Abased on a policy that that is deleted. Further, the PCE 104 may add acolumn based on a new domain and/or attribute and further, the PCE 104may remove a column based on a domain and/or attribute that is deleted,among other possibilities.

In some embodiments, the PCE 104 may perform an operation, such as amultiplication operation, with the matrix 150A and the matrix 150B toprovide and/or generate the matrix 150C. As such, the elements that arenot required are masked with the “0” based on the matrix operation toderive the matrix 150C, which may include only the elements required toprocess the transactions, thereby avoiding payload the duplication andredundancies described above. Further, the number of fields or data canbe reduced, resulting in less processing cycles and/or power required toprocess the data associated with the transaction represented by thematrices 150A, 150B, and 150C.

As shown, the matrix 150C is represented with the product-policy data130C, 132C, and 134C. The matrix 150C is represented with theproduct-policy data 130C in the first row including the data (c11), data(c12), data (c1j), and other data contemplated with the ellipses, andfurther the data (cm n), where the “j” is any number between 3 and “n,”and “n” represents the number of columns and the number of rows in thematrix 150C. The matrix 150C is represented with the product-policy data132C in the second row may include the data (ci1), data (ci2), data(cij), and other data contemplated with the ellipses, and further thedata (cin), where “i” is the particular row in the matrix 150C. Thematrix 150C is represented with the product-policy data 134C in thethird row may include the data (cn1), data (cn2), data (cnj), and otherdata contemplated with the ellipses, and further the data (cnn).

Notably, the matrix 150C also saves processing latency in various ordersof magnitude. Further, the matrix 150C may correspond to a name valuetuple that is standardized over numerous domains across platforms, suchas the platform 106 described above, such that the matrix 150C may becompressed and further interpreted by the various platforms. As such,the various platforms, including the platform 106, may interpret thespecific product configurations indicated by the product-policy data130C-134C based on the matrix 150C. As such, the time, the number ofcycles, and/or the power required to provision a product may beexceptionally less than conventional processes.

FIG. 2A illustrates an exemplary system 200, according to an embodiment.As shown, the system 200 may include an adaptive learning engine (ALE)202, a product configuration engine (PCE) 204, a monitoring platform206, and a network 207. Further, the ALE 202 may include an anomalydetector 214 and a machine learning component 216. Yet further, the PCE204 may include the product-policy data 220, 222, and/or 224, amongother data contemplated with the ellipses. In addition, there may be aconnection 208 between the ALE 202 and the platform 206, there may alsobe a connection 210 between the ALE 202 and the PCE 204, and there mayalso be a connection 212 between the PCE 204 and the platform 206.Notably, the ALE 202, the PCE 204, the monitoring platform 206, and theconnections 208-212 may take the form of ALE 102, the PCE 104, theplatform 106, and the connections 108-112, respectively, as describedabove in relation to FIGS. 1A-1C.

In some embodiments, the anomaly detector 214 may identify variousindications of anomalies associated with transactions. For example, thedetector 214 may identify transactions that may be declined,transactions that may be unauthorized, and/or other irregulartransactions, potentially over one or more periods of time, such ashours, days, and/or months. In some instances, the detector 214 may alsoidentify an increase in the number of declined transactions over aperiod of time, such as in an hour or possibly less time. Further, thedetector 214 may determine that the declined transactions are attributedto the token deficiencies described above. Yet further, the detector 214may determine the declined transactions are attributed to aninsufficient operating capacity for processing the transactions, amongother aspects of the system.

In some embodiments, the machine learning component 216 may beconfigured to learn various indications of anomalies, such as thosedescribed above in relation to the number of declined transactions. Insome instances, the component 216 may perform machine learning based onvarious self-learning mechanisms associated with detecting theindications of anomalies of the network 207 and applying a number ofintelligent rules, possibly to prevent valid transactions fromdeclining. Notably, the component 216 may include a neural networkconfigured to perform various system operations based on the learnedindications of anomalies and/or the number of learned rules. Inparticular, the component 216 may generate feedback data to variousother components of the system 200, such as the monitoring platform 206and/or the PCE 204, possibly over the connections 208 and/or 210,respectively. As noted, the learned indications of anomalies may bedisplayed by one or more devices, possibly to inform systemadministrators of the anomalies.

FIG. 2B illustrates the exemplary system 200 with the token trafficanalyzer (TTA) 234, according to an embodiment. As shown, the system 200may include the ALE 202, the PCE 204, the TTA 234, a token buffer 236,and a token database 238. In some embodiments, the TTA 234 is configuredto analyze token traffic associated with one or more networks 207,including various requests, such as token requests, requests to processtransactions with tokens, and/or requests for multiple servicesassociated with token verification, among other possibilities.

Further, the ALE 202 may include the anomaly detector 214 and themachine learning component 216, as described above. Yet further, the PCE204 may include the product-policy data 220, 222, and/or 224 describedabove. In addition, the PCE 204 may include the product-policy data 226,among other data contemplated with the ellipses below the data 226. Inaddition, there may also be a connection 210 between the ALE 202 and thePCE 204, a connection 240 between the ALE 202 and the TTA 234, aconnection 242 between the token database 238 and the PCE 204, aconnection 244 between the TTA 234 and the token database 238, and aconnection 246 between the ALE 202 and the token buffer 236.

In some embodiments, the system 200 may identify one or more tokenrequests, possibly where the tokens requested are received from thenetwork 207. For example, the tokens may be requested such that one ormore corresponding transactions in the network 207 may be processed. Assuch, the system 200 may determine an indication of token traffic in thenetwork 207. In particular, the TTA 234 may determine one or moreindicators, signs, and/or signals of token traffic based on the one ormore token requests, tokens issued over a period of time, and/or tokenstransferred over the network 207. Further, the system 200 may determinea token depletion rate based on the indication of token traffic in thenetwork 207 described above, such as the number of tokens issued overthe number of tokens generated in a given period of time. In someinstances, the system 200 may determine the token depletion rate withthe monitoring platform 206 based on the various indications of tokentraffic. As such, the system 200 may generate a number of tokens in thetoken buffer 236 and/or token database 238 in response to the tokendepletion rate. Further, the system 200 may learn one or moreindications of anomalies in the network 207 associated with the tokentraffic, such as the various anomalies described above possibly relatedto declined transactions. Notably, the system 200 may include anon-transitory memory and one or more hardware processors coupled to thenon-transitory memory. The processors may be configured to readinstructions from the non-transitory memory to cause the system 200 toperform the operations described herein.

In some embodiments, tokens may be associated with a given bin range.For example, considering the scenarios above, the number of tokensgenerated by the system 200 may be associated with a first bin range,possibly where the tokens are assigned respective identifiers based onthe first bin range. Further, the system 200 may detect a low tokenindication, such as a low token count, based on the token depletionrate. The low token indication may represent that there may not beenough tokens to process the requested transactions. The low tokenindication may include a sign and/or a signal indicating that a numberof the transactions may be declined based on the shortage of tokens. Forexample, the system 200 may detect a low token indication based on dataretrieved from the monitoring platform 206. As such, the system 200 mayprovision a second bin range for a second number of tokens based on thelow token indication. Further, the system 200 may generate the secondnumber of tokens in the token buffer 236 and/or the token database 238based on the second bin range. As shown, the product-policy data 226 maybe added to the PCE 204 based on the second number of tokens generatedfor the second bin range.

As noted, the system 200 may identify one or more declined transactions.In some instances, the declined transactions may be associated withbuyers, sellers, and/or partners of the transaction, among otherparticipating entities associated with the system 200. Further, thesystem 200 may identify the declined transactions based on the one ormore learned indications of anomalies in the network 207 describedabove, such as irregular activities including unauthorized transactions,irregular transactions, and/or insufficient system capacities to processthe transactions. As such, the system 200 may modify the product-policydata 220, 222, and/or 224 of the PCE 204 based on the one or moredeclined transactions, such that the declined transactions may beavoided.

Further, as noted, the system 200 may determine various operatingcapacities of the system 200. For example, the system 200 may determinean operating capacity associated with processing a number oftransactions with the system 200. In particular, the operating capacitymay be a higher operating capacity based on an increased number oftransactions, possibly where the number of transactions per second meetsor exceeds a given transaction threshold, e.g., 10,000 transactions persecond in one or more circumstances. Yet, in some instances, theoperating capacity may be a lower operating capacity based on adecreased number of transactions, possibly where the number oftransactions per second, e.g., 4,000 transactions per second, is belowthe transaction threshold described above to be 10,000 transactions persecond. In some instances, possibly where there is a lower operatingcapacity, the system 200 may determine a reserve capacity associatedwith the operating capacity, e.g., 6,000 transactions per second,possibly in response to the one or more learned indications of anomaliesin the network 207. As such, the system 200 may predict and/oranticipate that additional capacity may be needed in various other sitesof the system 200. Thus, the system 200 may share the reserve capacity,e.g., 6,000 transactions per second, with a number of sites associatedwith the system 200. For example, the system 200 may perform additionaltransactions associated with such sites based on the reserve capacity.

FIG. 2C illustrates the PCE 204 with matrices 250A, 250B, and 250C,according to an embodiment. In some embodiments, the system 200 mayoptimize various forms of data as described above in relation to FIGS.1A-1C. As such, the matrices 250A, 250B, and 250C may take the form ofthe matrices 150A, 150B, and 150C, respectively, as described above.

For example, the matrix 250A may include a number of values thatrepresent the core and/or profile elements described above. As shown,the matrix 250A is represented with product-policy data 220A in thefirst row including the data (a11), data (a12), data (a13), and otherdata contemplated with the ellipses, and further the data (a1n), where“n” is the number columns and the number rows in the matrix 250A.Product-policy data 222A corresponds to the second row including thedata (ai1), data (ai2), data (ai3), and other data contemplated with theellipses, and further the data (ain), where “i” is the particular row inthe matrix 250A. Product-policy data 224A corresponds to the third rowincluding the data (a(n−1)1), data (a(n−1)2), data (a(n−1)3), and otherdata contemplated with the ellipses, and further the data (a(n−1)(n−1)).Product-policy data 226A corresponds to the fourth row including thedata (an1), data (an2), data (an3), and other data contemplated with theellipses, and further the data (ann).

Further, the matrix 250B may also be a multi-dimensional matrix,possibly formed with inputs from the adaptive learning engine 202 overthe connection 210, as described above. In some embodiments, the system200 may determine and/or retrieve the feedback data 211 from the ALE202. In some instances, the feedback data 211 may be generated by themachine learning component 216 of the ALE 202, possibly indicating theone or more learned indications of anomalies. For example, the feedbackdata 211 may be determined based on the one or more learned indicationsof anomalies associated with the network 207, possibly indicating thedeclined transactions, the depleted number of tokens available, and/oran insufficient operating capacity to process the transactions, as notedabove. Yet further, the system 200 may determine the intermediate matrix250B that represents policy requirements in binary form based on thefeedback data 211 from the ALE 202.

The matrix 250B may be a representation of decision actions in one ormore policy domains provided in binary form. For example, the value of“1” may indicate that an element is required and the value of “0” mayindicate that an element is not required, as described above. As shown,the matrix 150B is represented with a first row including the data(loll), data (b12), data (b1j), and other data contemplated with theellipses, and further the data (bin), where the “j” is any numberbetween 3 and “n,” and where “n” may be the number of columns and thenumber of rows in the matrix 250B. The second row may include the data(bi1), data (bi2), data (bij), and other data contemplated with theellipses, and further the data (bin). The third row may include the data(b(n−1)1), data (b(n−1)2), data (b(n−1)(j−1)), and other datacontemplated with the ellipses, and further the data (b(n−1)(n−1)). Thefourth row may include the data (bn1), data (bn2), data (bnj), and otherdata contemplated with the ellipses, and further the data (bnn).

In some embodiments, the system 200 and/or the PCE 204 may perform anoperation, such as a multiplication operation, with the matrix 250A andthe matrix 250B to provide the matrix 250C. As such, the elements thatare not required are masked with the value of “0” based on the matrixoperation with the matrix 250B and the matrix 250C, where the matrix250C may include the elements required to process the transactions,thereby avoiding the payload duplication as described above. In someinstances, the matrix 250C may only include the elements required toprocess the transactions.

As shown, the matrix 250C is represented with the product-policy data220C in the first row including the data (c11), data (c12), data (c1j),and other data contemplated with the ellipses, and further the data(c1n), where the “j” is any number between 2 and “n,” and also where “n”may be the number of columns in the matrix 250C. The product-policy data222C is shown in the second row including the data (ci1), data (ci2),data (cij), and other data contemplated with the ellipses, and furtherthe data (cin). The product-policy data 224C is shown in the third rowincluding the data (c(n−1)1), data (c(n−1)2), data (c(n−1)(j−1)), andother data contemplated with the ellipses, and further the data(c(n−1)(n−1)). The product-policy data 226C is shown in the fourth rowmay include the data (cn1), data (cn2), data (cnj), and other datacontemplated with the ellipses, and further the data (cnn).

Notably, the matrix 250C may also save processing latency in variousorders of magnitude. Further, the matrix 250C may be standardized overnumerous domains across platforms, such as the platform 206 describedabove. As such, the various platforms, including the platform 206, mayrecognize the specific product and/or policy configurations indicated bythe matrix 250C. Based on removing the unnecessary elements, the time,processor cycles, and the processing power to provision a product willbe exceptionally less than conventional systems. For example, byremoving unnecessary elements, various data management operations may bereduced, such as the reduction in the number of data fields of thetransferred data, the amount of the data stored, and/or the dataprocessed, among additional operations related to data management.

In some embodiments, the system 200 may determine the first matrix 250Aassociated with the first product-policy data 220A-226A. Further, thesystem 200 may determine a second intermediate matrix 250B thatrepresents policy requirements in binary form based on feedback data 211from the ALE 202, possibly where the feedback data 211 may modify thepolicy requirements in real-time. As such, the system 200 may performone or more matrix operations, such as matrix multiplication, with thefirst matrix 250A and the second intermediate matrix 250B based on thefeedback data 211 from the ALE 202. Further, the system 200 may generatethe third output matrix 250C with second product-policy data 220C-226C.As noted, the system 200 may activate the second bin range for a secondnumber of tokens based on a low token indication detected. In suchinstances, the system 200 and/or the PCE 204 may generate theproduct-policy data 226C based on the second bin range. As shown in FIG.2B, the adaptive product-policy data 226 may be added, therebyunderscoring the addition of the second bin range.

In some embodiments, the system 200 may perform various operations basedon one or more declined transactions. For example, the system 200 maydetermine and/or identify the one or more declined transactions in thenetwork 207. Further, the system 200 may identify a number of anomaloustransactions in the network 207 based on the one or more declinedtransactions determined in the network 207. For example, the one or moredeclined transactions may be part of a large number of the anomaloustransactions in the network 207. As such, the system 200 may identifyone or more issuer systems associated with the number of anomaloustransactions, possibly where the issuer systems may be one or more rootcauses of the anomalous transactions. In some instances, the system 200may identify the issuer systems as potentially related to the number ofanomalous transactions.

In some embodiments, the system 200 may perform modifications based onone or more declined transactions. For example, referring back to FIG.2B, the system 200 may modify the product-policy data 220, 222, and/or224 of the PCE 204 based on the one or more identified issuer systemsassociated with the number of anomalous transactions. As noted, forexample, the product-policy data 226 may be added to the PCE 204,possibly based on the one or more identified issuer systems incurringtoken deficiencies. Yet further, the system 200 and/or the ALE 202 maylearn one or more system operations based on the one or more identifiedissuer systems associated with the number of anomalous transactions. Forexample, the system 200 may learn to provision additional bin rangesbased on the number of anomalous transactions, the token deficiencies,and/or the insufficient system capacities. Notably, a non-transitorymachine-readable medium of the system 200 may have stored thereonmachine-readable instructions executable to cause a machine to performthe various operations described herein.

In some embodiments, the system 200 may identify potential causes of theone or more declined transactions. For example, the system 200 maydetermine a token depletion rate based on the monitoring platform 206,where the platform 206 may monitor activities in the network 207. Forexample, the system 200 may determine the token depletion rateassociated with the one or more issuer systems described above. Asnoted, for example, the one or more issuer systems may incur a number oftoken deficiencies, thereby causing the number of anomalous transactionsidentified by the system 200. As such, the system 200 may generate anumber of tokens in the token buffer 236 and/or the token database 238based on the token depletion rate determined.

Yet further, in some instances, the number of generated tokens may beassociated with a first bin range. As such, the system 200 may detect alow token indication, such as the low token count described aboveassociated with the one or more issuer systems. In some instances, thelow token indication may be detected based on the token depletion rateassociated with the one or more issuer systems, potentially detectedbased on the activity monitor provided in the monitoring platform 206.In some instances, the system 200 may provision a second bin range for asecond number of tokens based on the low token indication. As such, thesystem 200 may generate the second number of tokens in the tokendatabase 238 based on the second bin range to process the transactions.

In some embodiments, the system 200 may activate the second bin rangefor the second number of tokens based on a low token indication detectedin association with the one or more issuer systems. As noted, the system200 may generate the second number of tokens in the token database 238based on the activated second bin range. Yet further, the system 200 maygenerate the product-policy data 226 based on the second bin range. Forexample, the product-policy data 226 may be generated based on the PCE204 of the system 200, thereby underscoring the addition of the secondbin range. As noted, in some instances, the bin and/or the bin ranges,may refer to a bank identification number, such as the “BIN” and/or theBIN range. For example, the bin and/or the bin ranges may follow theInternational Organization for Standardization (ISO), such as theISO/IEC 7812, that correspond to the number systems. Yet further, thebin and/or the bin ranges may also refer to an issuer identificationnumber and/or an “IIN,” possibly represented by six digits that alsoindicate the major industry identifier, where the IIN may identify theissuing organizations.

In some embodiments, the system 200 may learn from the one or moredeclined transactions, the number of anomalous transactions, tokendeficiencies possibly associated with the one or more issuer systems,and/or the insufficient system capacities. As such, the system 200 maydetermine the feedback data 211 from the ALE 202 based on the one ormore learned system operations, such as the operations described aboveto generate additional tokens. In some instances, the system 200 maydetermine and/or modify the intermediate matrix 250B that represents thepolicy requirements in binary form based on the feedback data 211,possibly based on the additional tokens generated. In particular, thematrix 250B may be modified to include elements required to process thedeclined transactions.

For example, the system 200 may determine the first matrix 250Aassociated with the first product-policy data 220A-226A. Further, thesystem 200 may determine the second intermediate matrix 250B thatrepresents the policy requirements described above to be in binary formbased on feedback data 211 from the ALE 202. As such, the system 200 mayperform one or more matrix operations, such as the matrixmultiplication, with the first matrix 250A and the second intermediatematrix 250B based on the feedback data 211. As such, the system 200 maygenerate the third output matrix 250C with the second product-policydata 220C-226C.

In some embodiments, the system 200 may balance various operatingcapacities across the system 200, possibly including the issuer systemsdescribed above. For example, the system 200 may determine an operatingcapacity associated with processing transactions with the system 200.Further, the system 200 may determine the reserve capacity from theoperating capacity, possibly based on the one or more learnedindications of anomalies in the network 207. Yet further, the system 200may share the reserve capacity with the one or more identified issuersystems, as described above, potentially where the identified issuersystems require the additional capacity to process the number ofdeclined transactions. As such, the system 200 may efficiently allocateits capacities to ensure operability across the identified issuersystems and/or other sites in the network 207.

FIG. 3 illustrates an exemplary system 300 with transfer data 302 and312, according to an embodiment. The system 300 may include aspects ofthe systems 100 and/or 200 described above. For example, the system 300may include the ALE 202, the PCE 204, the TTA 234, the token buffer 236,and/or the token database 238, among other components described herein.The data 302 may be transferred over the network 207 to process thetransaction data 306, possibly also referred to as the transaction 306.As shown, the transfer data 302 may include the header 304, thetransaction data 306, also possibly a trailer. Yet, the data 312 mayalso be transferred over the network 207 to process the transaction data316, possibly also referred to as the transaction 316. As shown, thetransfer data 312 may include the header 314, the transaction data 316,and also possibly a trailer.

Notably, the headers 304 and 314 may represent the core elementsdescribed above, possibly including various identifiers to move themoney assets. In particular, the identifiers may be provided with thetransaction data 306 and 316, respectively. In some instances, theheaders 304 and 314 may identify the buyer, the seller, and/or theconsumer, among other characteristics and/or attributes including theproducts and/or policy requirements associated with the transaction data306 and 316, respectively. For example, the headers 304 and 314 mayidentify the respective transaction data 306 and 316 as mass payments,multi-entity payments, a point-to-point payment, and/or a number ofcheckout processes, among numerous other possible product combinations.

In some embodiments, the transaction data 306 and 316 may be processedby the system 200 based on the respective headers 304 and 314. Forexample, the transaction data 306 may be processed differently than thetransaction data 316 based on the respective headers 304 and 314. Forinstance, consider the scenario where the header 304 indicates a masspayment, where a mass payment may be a payment for an amount that isgreater than one or more threshold amounts, possibly involving multipleentities. In such instances, various risk indications may be determinedbased on the type of payment involved with the data 306, potentially dueto the mass payment type. In another example, consider the scenariowhere the header 314 indicates that one of the participating entities isa preferred partner, possibly such that the processing of thetransaction 316 may be ensured and/or guaranteed.

In one example, consider that the headers 304 and 314 indicate masspayments. In such instances, the transfer data 302 may be generatedbased on the matrix 250A including the product-policy data 220A-226A, asdescribed above. Yet further, the transfer data 312 may be generatedbased on the matrix 250C, where various elements in the matrix 250C maybe masked based on the matrix multiplication with the matrix 250A andthe matrix 250B, as described above. As such, the header 314 may includeelements required to process the transaction data 316, thereby removingunnecessary data, as shown by the size of the header 314 and/or thetransaction data 316. For example, the size of the header 314 and thetransaction data 316 may be smaller than the header 304 and thetransaction 316. As such, the transfer data 312 may be smaller than thetransfer data 302, thereby making the storing, transferring, and/orprocessing of the data 312 more efficient. In particular, the system 300may utilize less memory to store the data 312, less processor cycles toprocess the data 312, and/or less time to process the data 312, therebyimproving the overall system capacity and optimizing system efficiency,potentially measured in transactions per second, as described above.

FIG. 4 illustrates an exemplary system 400, according to an embodiment.As shown, FIG. 4 includes an instrument 402, a data transfer 404, and amerchant page 406 providing an item 408. For example, consider ascenario where a user is browsing on the merchant page 406 andidentifies the item 408, possibly a laptop computer that the user may beinterested in purchasing. As such, the user may use the instrument 402to purchase the item 408. In particular, the data 404 transferred mayidentify details of the instrument 402 to the merchant page 406,possibly including a credit card number, an expiration date, and/or acard verification value (CVV) number.

In some embodiments, the merchant page 406 may transfer data 302 to thesystem 200. Notably, the data 302 may include the header 304 and thetransaction data 306 described above. Further, the system 200 shown mayinclude the product configuration engine (PCE) 204. Yet, as noted above,the PCE 204 may generate the transfer data 312 to process the requestedtransaction. As noted, the transfer data 312 may be generated based onthe matrix 250C described above, where various elements in the matrix250C may be masked based on the matrix multiplication with the matrix250A and the matrix 250B, as described above. As such, the matrix 250Cmay provide only the elements required to process the transaction data312, thereby removing unnecessary data, thereby improving the overallsystem capacity and optimizing the system efficiency as described abovein relation to FIG. 3. Notably, the system 400 may include aspects ofthe systems 100 and/or 200 described above. For example, the merchantpage 406 may available with the monitoring platform 206 and/or thenetwork 207 described above in relation to FIGS. 2A-2C.

FIG. 5 illustrates an exemplary method 500, according to an embodiment.The method 500 may be performed by the systems 100, 200, 300, and/or400. Notably, one or more steps of the method 400 described herein maybe omitted, performed in a different sequence, and/or combined withother methods for various types of applications contemplated herein,such as improving system capacities and/or efficiencies.

At step 502, the method 500 may include determine a request from a userdevice for one or more items on a webpage. For example, referring backto FIG. 4, the request from the user device may be for the item 408 onthe webpage 406.

At step 504, the method 500 may include determining data associated withan instrument based at least on the determined request. For example, thedata 404 may be determined to be associated with the instrument 402based on the request from the user device.

At step 506, the method 500 may include receiving first transfer datafrom the webpage based on the data associated with the instrument. Forexample, referring back to FIGS. 3-4, the first transfer data 302 may bereceived from the webpage 406 based on the data 404 associated with theinstrument 402.

At step 508, in response to receiving the first transfer data, themethod 500 may include generating second transfer data based on a numberof product-policy data of a product configuration engine. For example,as noted, the first transfer data 302 may be received by the system 200.Further, in response, the second transfer data 312 may be generated,possibly based on the product-policy data 220-226 and/or 220C-226Cdescribed above.

At step 510, the method 500 may include causing the user device todisplay a notification that indicates the request is granted based onthe second transfer data. In some instances, the user device may takethe form of a smartphone that displays the notification based on thesecond transfer data 312 described above.

In some embodiments, the method 500 may include steps associated withthe token depletion rate described above. For example, the method 500may include determining the token depletion rate associated with thesystem capacity required to grant the request for the one or more items408. For example, referring back to FIGS. 1A-4, the token depletion ratemay be determined by the system 200, possibly based on activity monitorof the platform 206, the token buffer 236, and/or the token database238. Further, the method 500 may include generating a number of tokensbased on the token depletion rate. As noted, the generated tokens may bestored in the token buffer 236 and/or the token database 238. As such,the request for the item 408 may be granted based on the number ofgenerated tokens.

In some embodiments, the method 500 may include steps associated withthe bin ranges. For example, a number of the generated tokens may beassociated with a first bin range. As such, the method 500 may includeprovisioning a second bin range for a second number of tokens. Forexample, the second bin range may be provisioned based on a low tokenindication, possibly determined in the one or more scenarios describedabove. As such, the method 500 may include generating a second number oftokens based on the second bin range. As such, the request for the item408 may be granted based on the second number of generated tokens.

In some embodiments, the method 500 may include steps to communicatewith the adaptive learning engines, such as the ALE 202 described above.For example, the method 500 may include determining and/or receiving thefeedback data 211 from the ALE 202. As noted, the feedback data 211 maybe based on one or more learned indications of anomalies in the network207, possibly associated with the request from the user device. Inparticular, the one or more learned indications of anomalies may bebased on an increase in the number of requests from various users of thesystem 200. As such, the method 500 may include determining and/ormodifying the intermediate matrix 250B that represents the policyrequirements to grant the request. In particular, the intermediatematrix 250B may be determined based on the feedback data 211 from theALE 202.

In some embodiments, the method 500 may also include steps to performthe matrix operations described above. For example, the method 500 mayinclude determining the first matrix 250A associated with firstproduct-policy data 220A-226A. Further, as noted, the method 500 mayinclude determining a second intermediate matrix 250B that representsthe policy requirements to grant the request, where the matrix 250B maybe determined based on feedback data 211 from the ALE 202. Further, themethod 500 may include performing one or more matrix operations with thefirst matrix 250A and the second intermediate matrix 250B based on thefeedback data 211. Yet further, the method 500 may include generatingthe third output matrix 250C with second product-policy data 220C-226C,where the second transfer data 312 may be generated based on the thirdoutput matrix 250C.

In some embodiments, the method 500 may also include steps to sharevarious system capacities with other sites. For example, the method 500may include determining an indication of an operating capacity of thesystem 200 associated with processing the request for one or more items408. Further, the method 500 may include determining a reserve capacityof the system 200 associated with the operating capacity. In particular,the reserved capacity may be determined in response to receiving thefeedback data 211 from the ALE 202 of the system 200, as describedabove. Yet further, the method 500 may include identifying a number ofsites associated with the system 200 that require additional capacity toprocess a number of transactions accordingly. As such, the method 500may include sharing the reserve capacity with the number of sites basedon the additional capacity required by the number of sites to processthe number of transactions.

FIG. 6 is a simplified block diagram of an exemplary system 600,according to an embodiment. The system 600 may include aspects of thesystems 100, 200, 300, and/or 400, among the other systems contemplatedherein. For example, the system 600 may include a server 602. The server602 may be configured to perform the operations described herein, suchas those involving the adaptive learning engine (ALE) 202 and theproduct configuration engine (PCE) 204, among other components describedabove. Yet further, the server 602 may be configured to perform suchoperations for a provider, such as PayPal, Inc. of San Jose, Calif.,USA. In addition, the system 600 may also include a client device 604and a client device 606. As such, the server 602 and the client devices604 and 606 may be configured to communicate over the one or morenetworks 608, possibly including the network 207 described above. Assuch, the system 600 includes multiple computing devices 602, 604,and/or 606.

The system 600 may operate with more or less than the computing devices602, 604, and/or 606 shown in FIG. 6, where each device may beconfigured to communicate over the one or more communication networks608, possibly to transfer data accordingly. The one or morecommunication networks 608 may include a packet-switched networkconfigured to provide digital networking communications and/or exchangedata of various forms, content, type, and/or structure. In someinstances, the one or more communication networks 608 may include a datanetwork, a private network, a local area network (LAN), a wide areanetwork (WAN), a telecommunications network, and/or a cellular network,among other possible networks. In some instances, the communicationnetwork 608 may include network nodes, web servers, switches, routers,base stations, microcells, and/or various buffers/queues to transferdata packets 622 and/or 624.

The data packets 622 and/or 624 may include the various forms of data,such as the data 302 and/or 312, respectively, possibly associated withthe user accounts. For example, the data packets 622 and/or 624 may betransferrable using communication protocols, such as packet layerprotocols, packet ensemble layer protocols, and/or network layerprotocols, among other types of communication protocols. For example,the data packets 622 and/or 624 may be transferrable using transmissioncontrol protocols and/or internet protocols (TCP/IP). In variousembodiments, each of the data packets 622 and 624 may be assembled ordisassembled into larger or smaller packets of varying capacities. Assuch, data packets 622 and/or 624 may be transferrable over the one ormore networks 608 and to various locations in the one or more networks608.

In some embodiments, the server 602 may take a variety of forms. Theserver 602 may be an enterprise server, possibly operable with one ormore operating systems to facilitate the scalability of the architectureassociated with the system 600. For example, the server 602 may operatewith a Unix-based operating system configured to integrate with agrowing number of servers in the one or more networks 608, the clientdevices 604 and/or 606, among other computing devices configured tocommunicate with the system 600. The server 602 may further facilitateworkloads associated with numerous data transfers in view of anincreasing item requests generated by the client devices 604 and/or 606.In particular, the server 602 may facilitate the scalability relative tosuch an increasing number of item requests to eliminate data congestion,bottlenecks, and/or transfer delays.

In some embodiments, the server 602 may include multiple components,such as one or more hardware processors 612, non-transitory memories614, non-transitory data storages 616, and/or communication interfaces618, among other possible components described in relation to FIG. 6,any of which may be communicatively linked via a system bus, network, orother connection mechanism 622. The one or more hardware processors 612may take the form of a multi-purpose processor, a microprocessor, aspecial purpose processor, a digital signal processor (DSP) and/or othertypes of processing components. For example, the one or more hardwareprocessors 612 may include an application specific integrated circuit(ASIC), a programmable system-on-chip (SOC), and/or a field-programmablegate array (FPGA). In particular, the one or more hardware processors612 may include a variable-bit (e.g., 64-bit) processor architectureconfigured to transfer the data packets 622 and/or 624. As such, the oneor more hardware processors 612 may execute varying instructions sets(e.g., simplified and complex instructions sets) with fewer cycles perinstruction than other general-purpose hardware processors, therebyimproving the performance of the server 602, possibly based on theoperations described herein. In practice, for example, the one or morehardware processors 612 may be configured to read instructions from thenon-transitory memory component 614 to cause the system 600 to performoperations as described herein.

The non-transitory memory component 614 and/or the non-transitory datastorage 616 may include one or more volatile, non-volatile, and/orreplaceable storage components, such as magnetic, optical, and/or flashstorage that may be integrated in whole or in part with the one or morehardware processors 612. Further, the memory component 614 may includeor take the form of a non-transitory computer-readable storage medium,having stored thereon computer-readable instructions that, when executedby the one or more hardware processors 612, cause the server 602 toperform operations described in this disclosure, illustrated by theaccompanying figures, and/or otherwise contemplated herein.

The communication interface component 618 may take a variety of formsand may be configured to allow the server 602 to communicate with one ormore devices, such as the client devices 604 and/or 606. For example,the communication interface 618 may include a transceiver that enablesthe server 602 to communicate with the client devices 604 and/or 606over the one or more networks 608. In some instances, the communicationinterface 618 may include a wired interface, such as an Ethernetinterface, to communicate with the client devices 604 and/or 606.Further, in some instances, the communication interface 618 may includea cellular interface, such as a Global System for Mobile Communications(GSM) interface, a Code Division Multiple Access (CDMA) interface,and/or a Time Division Multiple Access (TDMA) interface. Yet further, insome instances, the communication interface 618 may include a local areanetwork interface, such as a WI-FI interface configured to communicatewith a number of different protocols. As such, the communicationinterface 618 may include a wireless interface operable to transfer dataover short distances utilizing short-wavelength radio waves inapproximately the 2.4 to 2.485 GHz range. In some instances, thecommunication interface 618 may send/receive data or data packets 622and/or 624 to/from client devices 604 and/or 606.

The client devices 604 and 606 may also be configured to perform avariety of operations such as those described in this disclosure,illustrated by the accompanying figures, and/or otherwise contemplatedherein. In particular, the client devices 604 and 606 may be configuredto transfer data packets 622 and/or 624 to and from the server 602. Thedata packets 622 and/or 624 may also include location data associatedwith the request items, such as Global Positioning System (GPS) data orGPS coordinate data. Yet further, the data packets 622 and/or 624 mayinclude environmental data including triangulation data, beacon data,WI-FI data, temperature data, and/or sensor data. As noted, the datapackets 622 and/or 624 may include the transfer data 302 and/or 312among other types of data.

In some embodiments, the client devices 604 and 606 may include or takethe form of a smartphone system, a personal computer (PC), such as alaptop device, a tablet computer device, a wearable computer device, ahead-mountable display (HMD) device, a smart watch device, and/or othertypes of computing devices configured to transfer data associated with auser account. The client devices 604 and 606 may include variouscomponents, including, for example, input/output (I/O) interfaces 630and 640, communication interfaces 632 and 642, hardware processors 634and 644, and non-transitory data storages 636 and 646, respectively, allof which may be communicatively linked with each other via a system bus,network, or other connection mechanisms 638 and 648, respectively.

The I/O interfaces 630 and 640 may be configured to receive inputs fromand provide outputs to users of the client devices 604 and 606. Forexample, the I/O interface 630 may include a graphical user interface(GUI) configured to receive a user input that activates the providerapplication with the other applications. Thus, the I/O interfaces 630and 640 may include displays and/or input hardware with tangiblesurfaces, such as touchscreens with touch sensors and/or proximitysensors configured with variable sensitivities to detect the touchinputs from a user. The I/O interfaces 630 and 640 may also be synchedwith a microphone, sound speakers, and/or other audio mechanismsconfigured to receive voice commands. Further, the I/O interfaces 630and 640 may also include a computer mouse, a keyboard, and/or otherinterface mechanisms. In addition, I/O interfaces 630 and 640 mayinclude output hardware, such as one or more touchscreen displays,haptic feedback systems, and/or other hardware components.

In some embodiments, communication interfaces 632 and 642 may take avariety of forms. For example, communication interfaces 632 and 642 maybe configured to allow client devices 604 and 606, respectively, tocommunicate with one or more devices according to a number of protocolsdescribed or contemplated herein. For instance, communication interfaces632 and 642 may be configured to allow client devices 604 and 606,respectively, to communicate with the server 602 via the one or morecommunication networks 608. The processors 634 and 644 may include oneor more multi-purpose processors, microprocessors, special purposeprocessors, digital signal processors (DSP), application specificintegrated circuits (ASIC), programmable system-on-chips (SOC),field-programmable gate arrays (FPGA), and/or other types of processingcomponents described or contemplated herein.

The data storages 636 and 646 may include one or more volatile,non-volatile, removable, and/or non-removable storage components, andmay be integrated in whole or in part with processors 634 and 644,respectively. Further, data storages 636 and 646 may include or take theform of non-transitory computer-readable mediums, having stored thereoninstructions that, when executed by processors 634 and 644, cause theclient devices 604 and 606 to perform operations, respectively, such asthose described in this disclosure, illustrated by the accompanyingfigures, and/or otherwise contemplated herein.

In some embodiments, the user device 604 may generate a request for theone or more items 408 with a user account. For example, the generatedrequest may be encoded in the data packet 622 to establish a connectionwith the server 602. As such, the data packet 622 may initiate a searchof an internet protocol (IP) address of the server 602 that may take theform of the IP address, 192.168.1.102, for example. In some instances,an intermediate server, e.g., a domain name server (DNS) and/or a webserver, possibly in the one or more networks 608 may identify the IPaddress of the server 602 to establish the connection between the userdevice 604 and the server 602. As such, the server 602 may grant therequest for the one or more items 408.

It can be appreciated that the server 602 and the user devices 604 and606 may be deployed in various other ways. For example, the operationsperformed by the server 602 and/or the user devices 604 and 606 may beperformed by a greater or a fewer number of devices. Further, theoperations performed by two or more of the systems and/or devices 602,604, and/or 606 may be combined and performed by a single device. Yetfurther, the operations performed by a single device may be separated ordistributed among the server 602, the user devices 604 and 606.

FIG. 7 illustrates the user device 604, according to an embodiment. Asshown, the user device 604 may take the form of a smartphone. Yetfurther, the user device 604 may include aspects of the device 604described above in relation to FIG. 6. For example, the user device 604may include the I/O interface 640, which may include the graphical userinterface 640A, the speaker 640B, the side buttons 640C, and the button640D that may include a fingerprint sensor.

As shown, the interface 640A displays the merchant page 406 and therequested item 408 as described above. Further, the interface 640displays the notification 702 that indicates the request for the item408 is approved or granted. As noted, referring back to FIG. 4, the data302 may be received by the system 200 and the data 312 may betransferred to the page 406 to process the transaction for the item 408.As such, the notification 702 indicates that the request for the item408 is approved and the transaction is processed. In some instances, thetransaction may be processed with funds from the user's account.

Notably, a user account associated with a provider may be displayed onthe client device 604, possibly with the I/O interface 630. For example,a provider application of the user device 604 may be configured toaccess the user account displayed on the I/O interface 630. In someinstances, the user account may be a personal account associated withfunds. Further, the user account may be a corporate account, such thatemployees, staff, worker personnel, and/or contractors, among otherindividuals may have access to the corporate account. Further, anaccount may be a family account created for multiple family members,where each member may have access to the account. Yet further, it shouldbe noted that a user may be a number of individuals, a group, and/orpossibly a robot, a robotic device, and/or a robotic system, among othercomputing devices capable of transferring data associated with the useraccount. In some instances, login data may be required to access theuser account and/or perform a transfer with the account. For example,the data required may include credential information, such as a login,an email address, a username, a password, a phone number, a securitycode, an encryption key, authentication data, biometric data (e.g.,fingerprint data), and/or other types of data to access the user accountand/or perform a transfer with the account.

The present disclosure, the accompanying figures, and the claims are notintended to limit the present disclosure to the precise forms orparticular fields of use disclosed. As such, it is contemplated thatvarious alternate embodiments and/or modifications to the presentdisclosure, whether explicitly described or implied herein, are possiblein light of the disclosure. Having thus described embodiments of thepresent disclosure, persons of ordinary skill in the art will recognizethat changes may be made in form and detail without departing from thescope of the present disclosure.

The invention claimed is:
 1. A system comprising: a non-transitorymemory; and one or more hardware processors coupled to thenon-transitory memory and configured to read instructions from thenon-transitory memory to cause the system to perform operationscomprising: determining one or more declined transactions in a network;identifying a plurality of anomalous transactions in the network basedat least on the one or more declined transactions; identifying one ormore issuer systems associated with the plurality of anomaloustransactions in the network; learning one or more system operationsassociated with the one or more issuer systems based on the plurality ofanomalous transactions; modifying product-policy data for the one ormore identified issuer systems based on the one or more systemoperations; receiving, via a merchant webpage, transaction dataassociated with a transaction request, wherein the transaction datacomprises a header and a body; modifying the header based on themodified product-policy data; and transmitting the modified transactiondata to an issuer system of the one or more issuer systems.
 2. Thesystem of claim 1, wherein the operations further comprise: determininga token depletion rate associated with the one or more issuer systemsbased at least on the plurality of anomalous transactions; andgenerating a plurality of tokens in a token database based at least onthe token depletion rate.
 3. The system of claim 2, wherein theplurality of tokens is associated with a first bin range, and whereinthe operations further comprise: detecting a low token indication basedat least on the token depletion rate; provisioning a second bin rangefor a second plurality of tokens in response to the detecting the lowtoken indication; and generating the second plurality of tokens in thetoken database based on the second bin range.
 4. The system of claim 1,wherein the operations further comprise: detecting a low tokenindication based on the plurality of anomalous transactions; andactivating a bin range for a plurality of tokens based at least on thelow token indication, wherein the modifying the product-policy data isfurther based on the activated bin range.
 5. The system of claim 1,wherein the operations further comprise: determining feedback data basedat least on the learning the one or more system operations associatedwith the one or more issuer systems; and determining an intermediatematrix that represents policy requirements in a binary form based atleast on the feedback data, wherein the product-policy data is modifiedfurther using the intermediate matrix.
 6. The system of claim 5, whereinthe operations further comprise: determining a first matrix associatedwith the product-policy data; and generating a second matrix based onperforming one or more matrix operations with the first matrix and theintermediate matrix, wherein the product-policy data is modified furtherbased on the second matrix.
 7. The system of claim 1, wherein theoperations further comprise: determining an operating capacity that thesystem has in processing transactions; determining a reserve capacityfor the system based at least on the learning the one or more systemoperations associated with the one or more issuer systems; and sharingthe reserve capacity with the one or more issuer systems.
 8. A method,comprising: determining, by one or more hardware processors, one or moredeclined transactions in a network; identifying, by the one or morehardware processors, a plurality of anomalous transactions in thenetwork based on the one or more declined transactions; identifying, bythe one or more hardware processors, one or more issuer systemsassociated with the plurality of anomalous transactions in the network;modifying, by the one or more hardware processors, product-policy dataof a product configuration engine for the one or more identified issuersystems based on the plurality of anomalous transactions; receiving, bythe one or more hardware processors via a merchant webpage, transactiondata associated with a transaction request, wherein the transaction datacomprises a header and a body; modifying, by the one or more hardwareprocessors, the header based on the modified product-policy data; andtransmitting, by the one or more hardware processors, the modifiedtransaction data to an issuer system of the one or more issuer systems.9. The method of claim 8, further comprising: determining a tokendepletion rate associated with the one or more issuer systems based atleast on the plurality of anomalous transactions; and generating aplurality of tokens in a token database based at least on the tokendepletion rate.
 10. The method of claim 9, wherein the plurality oftokens is associated with a first bin range, and wherein the methodfurther comprises: detecting a low token indication based at least onthe token depletion rate; provisioning a second bin range for a secondplurality of tokens in response to the detecting the low tokenindication; and generating the second plurality of tokens in the tokendatabase based on the second bin range.
 11. The method of claim 8,further comprising: detecting a low token indication based on theplurality of anomalous transactions; and activating a bin range for aplurality of tokens based at least on the low token indication, whereinthe modifying the product-policy data is further based on the activatedbin range.
 12. The method of claim 8, further comprising: determiningfeedback data based at least on the learning the one or more systemoperations; and determining an intermediate matrix that representspolicy requirements in a binary form based at least on the feedbackdata, wherein the product-policy data is modified further using theintermediate matrix.
 13. The method of claim 12, further comprising:determining a first matrix associated with the product-policy data; andgenerating a second matrix based on performing one or more matrixoperations with the first matrix and the intermediate matrix, whereinthe product-policy data is modified further based on the second matrix.14. The method of claim 8, further comprising: determining an operatingcapacity associated with processing transactions by a plurality ofissuer systems; determining a reserve capacity for the one or moreissuer systems based at least on the learning the one or more systemoperations; and sharing the reserve capacity with the one or more issuersystems.
 15. A non-transitory machine-readable medium having storedthereon machine-readable instructions executable to cause a machine toperform operations comprising: determining one or more declinedtransactions in a network; identifying a plurality of anomaloustransactions in the network based at least on the one or more declinedtransactions; identifying one or more issuer systems associated with theplurality of anomalous transactions in the network; modifyingproduct-policy data for the one or more identified issuer systems basedon the plurality of anomalous transactions; receiving, via a merchantwebpage, transaction data associated with a transaction request, whereinthe transaction data comprises a header and a body; modifying the headerbased on the modified product-policy data; and transmitting the modifiedtransaction data to an issuer system of the one or more issuer systems.16. The non-transitory machine-readable medium of claim 15, wherein theoperations further comprise: determining a token depletion rateassociated with the one or more issuer systems based at least on theplurality of anomalous transactions; and generating a plurality oftokens in a token database based at least on the token depletion rate.17. The non-transitory machine-readable medium of claim 16, wherein theplurality of tokens is associated with a first bin range, and whereinthe operations further comprise: detecting a low token indication basedat least on the token depletion rate; provisioning a second bin rangefor a second plurality of tokens in response to the detecting the lowtoken indication; and generating the second plurality of tokens in thetoken database based on the second bin range.
 18. The non-transitorymachine-readable medium of claim 15, wherein the operations furthercomprise: detecting a low token indication based on the plurality ofanomalous transactions; and activating a bin range for a plurality oftokens based at least on the low token indication, wherein the modifyingthe product-policy data is further based on the activated bin range. 19.The non-transitory machine-readable medium of claim 15, wherein theoperations further comprise: determining feedback data based at least onthe learning the one or more system operations; and determining anintermediate matrix that represents policy requirements in a binary formbased at least on the feedback data, wherein the product-policy data ismodified further using the intermediate matrix.
 20. The non-transitorymachine-readable medium of claim 19, wherein the operations furthercomprise: determining a first matrix associated with the product-policydata; and generating a second matrix based on performing one or morematrix operations with the first matrix and the intermediate matrixwherein the product-policy data is modified further based on the secondmatrix.